In this context, Blockchain is a promising technology for enabling reliable decentralized FL via its peer-to-peer (P2P) networking topology empowered by an immutable, transparent and tamper-proof data ledger [1]}. The use of Blockchain in FL can mitigate the single-point-of-failure issue, and builds trust between devices and multiple servers for secure ML model training [2]}. Given these benefits, Blockchain-based federated learning (BFL) has been recently investigated in different domains, such as vehicular communications [3]} and mobile crowdsensing [4]}. To implement BFL systems, devices need to interface with decentralized servers in settings where communication and computation resources are more limited, which will impact the ML model training quality. blackMoreover, each device may be keen in participating in block mining to further gain blockchain rewards, e.g., cryptocurrency tokens, which in turn enhances the reliability and security for FL. This leads to the concept of mobile mining which has been adopted in practical BFL environments [5]}, [6]}. The concurrence of both ML model training and block mining introduces new challenges to network service latency and resource management, motivating a holistic optimization architecture for efficient BFL in wireless networks.
